Topic: Heart Failure Prediction

The goal of this study is to develop and evaluate machine learning predictive models for predicting heart failure survival.

Data Information

age: Age of the patient anaemia: Haemoglobin level of patient (Boolean) creatinine_phosphokinase: Level of the CPK enzyme in the blood (mcg/L) diabetes: If the patient has diabetes (Boolean) ejection_fraction: Percentage of blood leaving the heart at each contraction high_blood_pressure: If the patient has hypertension (Boolean) platelets: Platelet count of blood (kiloplatelets/mL) serum_creatinine: Level of serum creatinine in the blood (mg/dL) serum_sodium: Level of serum sodium in the blood (mEq/L) sex: Sex of the patient smoking: If the patient smokes or not (Boolean) time: Follow-up period (days) DEATH_EVENT: If the patient deceased during the follow-up period (Boolean) [Attributes having Boolean values: 0 = Negative (No); 1 = Positive (Yes)]

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline
from sklearn.metrics import classification_report, confusion_matrix

data = 'heartfailure.csv'
df= pd.read_csv(data)

df.head()

	age	anaemia	creatinine_phosphokinase	diabetes	ejection_fraction	high_blood_pressure	platelets	serum_creatinine	serum_sodium	sex	smoking	time	DEATH_EVENT
0	75.0	0	582	0	20	1	265000.00	1.9	130	1	0	4	1
1	55.0	0	7861	0	38	0	263358.03	1.1	136	1	0	6	1
2	65.0	0	146	0	20	0	162000.00	1.3	129	1	1	7	1
3	50.0	1	111	0	20	0	210000.00	1.9	137	1	0	7	1
4	65.0	1	160	1	20	0	327000.00	2.7	116	0	0	8	1

Data Analysis

df.isnull().sum() #no missing value

age                         0
anaemia                     0
creatinine_phosphokinase    0
diabetes                    0
ejection_fraction           0
high_blood_pressure         0
platelets                   0
serum_creatinine            0
serum_sodium                0
sex                         0
smoking                     0
time                        0
DEATH_EVENT                 0
dtype: int64

df.shape #299 observations and 13 features

(299, 13)

df.columns

Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',
       'ejection_fraction', 'high_blood_pressure', 'platelets',
       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',
       'DEATH_EVENT'],
      dtype='object')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 299 entries, 0 to 298
Data columns (total 13 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   age                       299 non-null    float64
 1   anaemia                   299 non-null    int64  
 2   creatinine_phosphokinase  299 non-null    int64  
 3   diabetes                  299 non-null    int64  
 4   ejection_fraction         299 non-null    int64  
 5   high_blood_pressure       299 non-null    int64  
 6   platelets                 299 non-null    float64
 7   serum_creatinine          299 non-null    float64
 8   serum_sodium              299 non-null    int64  
 9   sex                       299 non-null    int64  
 10  smoking                   299 non-null    int64  
 11  time                      299 non-null    int64  
 12  DEATH_EVENT               299 non-null    int64  
dtypes: float64(3), int64(10)
memory usage: 30.5 KB

#to check the datatype
df.dtypes

age                         float64
anaemia                       int64
creatinine_phosphokinase      int64
diabetes                      int64
ejection_fraction             int64
high_blood_pressure           int64
platelets                   float64
serum_creatinine            float64
serum_sodium                  int64
sex                           int64
smoking                       int64
time                          int64
DEATH_EVENT                   int64
dtype: object

#Let's compare the death (1) and survival (0)
death_rate = df.DEATH_EVENT.value_counts()/299
death_rate

0    0.67893
1    0.32107
Name: DEATH_EVENT, dtype: float64

#About 68% survived while 32% died from heart failure

sns.countplot(x="DEATH_EVENT", data = df)
plt.title('Distribution of Heart Failure')
plt.show()

#203 survive 96 death

#Analysing age feature
df['age'].min(), df['age'].mean() , df['age'].max() 

(40.0, 60.83389297658862, 95.0)

#The minimim age we have for any patient is 40, mean age as 60 and maximum as 95 .
#Majority of the patient's age lies near 60-61 Years.

sns.displot(df, x="age")
plt.title('Age Distribution')
plt.show()

#We can also confirm from above plot, that the majority of patient lies at age near 60-61 Years Age.

#Finding the relationship between age and heart failure

sns.displot(df, x="age", hue = 'DEATH_EVENT')
plt.title('Age and Heart Failure')
plt.show()

#age 79 to 95 are more likely to die
#age 40 to 78 are going to survive

#Gender
sns.countplot(x="sex", data = df)
plt.title('Distribution of Sex Feature')
plt.show()

#majority of the population is female

sns.countplot(x="sex", hue = 'DEATH_EVENT', data = df)
plt.title('Sex vs Heart Disease')
plt.show()

#male has more deathevent and high survival but low in female

#highblod pressure Distribution
sns.countplot(x="high_blood_pressure", data = df)
plt.title('Distribution of high blood pressure Feature')
plt.show()

sns.displot(df, x="high_blood_pressure", hue = 'DEATH_EVENT')
plt.title('High Blood Pressure and Heart Failure')
plt.show()

sns.displot(df, x="time", hue = 'DEATH_EVENT')
plt.title('time and Heart Failure')
plt.show()

# Feature importance and Selection

plt.rcParams['figure.figsize']=10,4 
sns.set_style("darkgrid")

x = df.iloc[:, :-1]
y = df.iloc[:,-1]

from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
model = ExtraTreesClassifier()
model.fit(x,y)
print(model.feature_importances_) 
feat_importances = pd.Series(model.feature_importances_, index=x.columns)
feat_importances.nlargest(12).plot(kind='barh')

[0.09949726 0.02733504 0.07899392 0.02788416 0.12244019 0.0207539
 0.07382136 0.12237323 0.08266914 0.02624013 0.02920331 0.28878836]

<AxesSubplot:>

# We will select only features : time, ejection_fraction, serum_creatinine, age

#To check outliers
#Boxplot for ejection_fraction

sns.boxplot(x = df.ejection_fraction, color = 'teal')
plt.show()

#there are tow outliers and we can remove them

df[df['ejection_fraction']>=70]

	age	anaemia	creatinine_phosphokinase	diabetes	ejection_fraction	high_blood_pressure	platelets	serum_creatinine	serum_sodium	sex	smoking	time	DEATH_EVENT
64	45.0	0	582	0	80	0	263358.03	1.18	137	0	0	63	0
217	54.0	1	427	0	70	1	151000.00	9.00	137	0	0	196	1

#to keep our data for the age below 70
df = df[df['ejection_fraction']<70]

#let's check the outlier for time
sns.boxplot(x=df.time, color = 'teal')
plt.show()

#there is no outlier for time

#let's find the outlier for serum creatinine
# Boxplot for serum creatinine
sns.boxplot(x=df.serum_creatinine, color = 'teal')
plt.show()

#domain knowledeg shows serum creatinine vlues there are no outliers but datapoints that assist in predicting death events

import plotly.graph_objects as go
from plotly.subplots import make_subplots

d1 = df[(df["DEATH_EVENT"]==0) & (df["sex"]==1)]
d2 = df[(df["DEATH_EVENT"]==1) & (df["sex"]==1)]
d3 = df[(df["DEATH_EVENT"]==0) & (df["sex"]==0)]
d4 = df[(df["DEATH_EVENT"]==1) & (df["sex"]==0)]

label1 = ["Male","Female"]
label2 = ['Male - Survived','Male - Died', "Female -  Survived", "Female - Died"]
values1 = [(len(d1)+len(d2)), (len(d3)+len(d4))]
values2 = [len(d1),len(d2),len(d3),len(d4)]

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=label1, values=values1, name="GENDER"),
              1, 1)
fig.add_trace(go.Pie(labels=label2, values=values2, name="GENDER VS DEATH_EVENT"),
              1, 2)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent")

fig.update_layout(
    title_text="GENDER DISTRIBUTION IN THE DATASET  \
                   GENDER VS DEATH_EVENT",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text='GENDER', x=0.19, y=0.5, font_size=10, showarrow=False),
                 dict(text='GENDER VS DEATH_EVENT', x=0.84, y=0.5, font_size=9, showarrow=False)],
    autosize=False,width=1200, height=500, paper_bgcolor="white")

fig.show()

#65.3% are male where 44.4% survived and 20.9% died, 34.7% female 23.6% survived and 11.1% died

import plotly.graph_objects as go
from plotly.subplots import make_subplots

d1 = df[(df["DEATH_EVENT"]==0) & (df["diabetes"]==1)]
d2 = df[(df["DEATH_EVENT"]==1) & (df["diabetes"]==1)]
d3 = df[(df["DEATH_EVENT"]==0) & (df["diabetes"]==0)]
d4 = df[(df["DEATH_EVENT"]==1) & (df["diabetes"]==0)]
label1 = ["No Diabetes","Diabetes"]
label2 = ['No Diabetes - Survived','Diabetes - Survived', "No Diabetes -  Died", "Diabetes  - Died"]
values1 = [(len(d1)+len(d3)), (len(d2)+len(d4))]
values2 = [len(d1),len(d2),len(d3),len(d4)]

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=label1, values=values1, name="DIABETES"),
              1, 1)
fig.add_trace(go.Pie(labels=label2, values=values2, name="DIABETES VS DEATH_EVENT"),
              1, 2)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent")

fig.update_layout(
    title_text="DIABETES DISTRIBUTION IN THE DATASET \
                  DIABETES VS DEATH_EVENT",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text='DIABETES', x=0.20, y=0.5, font_size=10, showarrow=False),
                 dict(text='DIABETES VS DEATH_EVENT', x=0.84, y=0.5, font_size=8, showarrow=False)],
    autosize=False,width=1200, height=500, paper_bgcolor="white")
fig.show()

#hint: 57.9% are NON DIABETIC (out of which 39.4% survived and 18.5% died) and 42.1% are DIABETIC (out of which 28.6% survived and 13.5% died).

import plotly.graph_objects as go
from plotly.subplots import make_subplots

d1 = df[(df["DEATH_EVENT"]==0) & (df["anaemia"]==1)]
d2 = df[(df["DEATH_EVENT"]==1) & (df["anaemia"]==1)]
d3 = df[(df["DEATH_EVENT"]==0) & (df["anaemia"]==0)]
d4 = df[(df["DEATH_EVENT"]==1) & (df["anaemia"]==0)]

label1 = ["No Anaemia","Anaemia"]
label2 = ['No Anaemia - Survived','No Anaemia - Died', "Anaemia -  Survived", "Anaemia  - Died"]
values1 = [(len(d1)+len(d2)), (len(d3)+len(d4))]
values2 = [len(d1),len(d2),len(d3),len(d4)]

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=label1, values=values1, name="ANAEMIA"),
              1, 1)
fig.add_trace(go.Pie(labels=label2, values=values2, name="ANAEMIA VS DEATH_EVENT"),
              1, 2)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent")

fig.update_layout(
    title_text="ANAEMIA DISTRIBUTION IN THE DATASET \
                  ANAEMIA VS DEATH_EVENT",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text='ANAEMIA', x=0.20, y=0.5, font_size=10, showarrow=False),
                 dict(text='ANAEMIA VS DEATH_EVENT', x=0.84, y=0.5, font_size=8, showarrow=False)],
    autosize=False,width=1200, height=500, paper_bgcolor="white")
fig.show()

#hint: 56.9% are NON ANAEMIC (out of which 40.1% survived and 16.8% died) and 43.1% are ANAEMIC (out of which 27.9% survived and 15.2% died).

import plotly.graph_objects as go
from plotly.subplots import make_subplots

d1 = df[(df["DEATH_EVENT"]==0) & (df["smoking"]==1)]
d2 = df[(df["DEATH_EVENT"]==1) & (df["smoking"]==1)]
d3 = df[(df["DEATH_EVENT"]==0) & (df["smoking"]==0)]
d4 = df[(df["DEATH_EVENT"]==1) & (df["smoking"]==0)]

label1 = ["No Smoking","Smoking"]
label2 = ['No Smoking - Survived','No Smoking - Died', "Smoking - Survived", "Smoking - Died"]
values1 = [(len(d1)+len(d2)), (len(d3)+len(d4))]
values2 = [len(d1),len(d2),len(d3),len(d4)]

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=label1, values=values1, name="SMOKING"),
              1, 1)
fig.add_trace(go.Pie(labels=label2, values=values2, name="SMOKING VS DEATH_EVENT"),
              1, 2)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent")

fig.update_layout(
    title_text="SMOKING DISTRIBUTION IN THE DATASET \
                  SMOKING VS DEATH_EVENT",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text='SMOKING', x=0.20, y=0.5, font_size=10, showarrow=False),
                 dict(text='SMOKING VS DEATH_EVENT', x=0.84, y=0.5, font_size=8, showarrow=False)],
    autosize=False,width=1200, height=500, paper_bgcolor="white")
fig.show()

# hint: 67.7% do not SMOKE (out of which 45.8% survived and 21.9% died) and 32.3% do SMOKE (out of which 22.2% survived and 10.1% died).

import plotly.graph_objects as go
from plotly.subplots import make_subplots

d1 = df[(df["DEATH_EVENT"]==0) & (df["high_blood_pressure"]==1)]
d2 = df[(df["DEATH_EVENT"]==1) & (df["high_blood_pressure"]==1)]
d3 = df[(df["DEATH_EVENT"]==0) & (df["high_blood_pressure"]==0)]
d4 = df[(df["DEATH_EVENT"]==1) & (df["high_blood_pressure"]==0)]

label1 = ["No High BP","High BP"]
label2 = ['No High BP - Survived','No High BP - Died', "High BP -  Survived", "High BP  - Died"]
values1 = [(len(d1)+len(d2)), (len(d3)+len(d4))]
values2 = [len(d1),len(d2),len(d3),len(d4)]

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=label1, values=values1, name="HIGH BP"),
              1, 1)
fig.add_trace(go.Pie(labels=label2, values=values2, name="HIGH BP VS DEATH_EVENT"),
              1, 2)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent")

fig.update_layout(
    title_text="HIGH BP DISTRIBUTION IN THE DATASET \
                  HIGH BP VS DEATH_EVENT",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text='HIGH BP', x=0.20, y=0.5, font_size=10, showarrow=False),
                 dict(text='HIGH BP VS DEATH_EVENT', x=0.84, y=0.5, font_size=8, showarrow=False)],
    autosize=False,width=1200, height=500, paper_bgcolor="white")
fig.show()

#hint: 65% do not have HIGH BLOOD PRESSURE (out of which 45.8% survived and 19.2% died) and 35% have HIGH BLOOD PRESSURE (out of which 22.2% survived and 12.8% died).

# Doing Univariate Analysis for statistical description and understanding of dispersion of data
df.describe().T

	count	mean	std	min	25%	50%	75%	max
age	297.0	60.910215	11.892512	40.0	51.0	60.0	70.0	95.0
anaemia	297.0	0.430976	0.496049	0.0	0.0	0.0	1.0	1.0
creatinine_phosphokinase	297.0	582.360269	973.518622	23.0	115.0	249.0	582.0	7861.0
diabetes	297.0	0.420875	0.494533	0.0	0.0	0.0	1.0	1.0
ejection_fraction	297.0	37.835017	11.470426	14.0	30.0	38.0	45.0	65.0
high_blood_pressure	297.0	0.350168	0.477828	0.0	0.0	0.0	1.0	1.0
platelets	297.0	263736.339125	97915.822481	25100.0	213000.0	262000.0	304000.0	850000.0
serum_creatinine	297.0	1.368990	0.938731	0.5	0.9	1.1	1.4	9.4
serum_sodium	297.0	136.622896	4.427251	113.0	134.0	137.0	140.0	148.0
sex	297.0	0.653199	0.476755	0.0	0.0	1.0	1.0	1.0
smoking	297.0	0.323232	0.468500	0.0	0.0	0.0	1.0	1.0
time	297.0	130.265993	77.683871	4.0	73.0	115.0	205.0	285.0
DEATH_EVENT	297.0	0.319865	0.467211	0.0	0.0	0.0	1.0	1.0

df.describe()

	age	anaemia	creatinine_phosphokinase	diabetes	ejection_fraction	high_blood_pressure	platelets	serum_creatinine	serum_sodium	sex	smoking	time	DEATH_EVENT
count	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000	297.000000
mean	60.910215	0.430976	582.360269	0.420875	37.835017	0.350168	263736.339125	1.368990	136.622896	0.653199	0.323232	130.265993	0.319865
std	11.892512	0.496049	973.518622	0.494533	11.470426	0.477828	97915.822481	0.938731	4.427251	0.476755	0.468500	77.683871	0.467211
min	40.000000	0.000000	23.000000	0.000000	14.000000	0.000000	25100.000000	0.500000	113.000000	0.000000	0.000000	4.000000	0.000000
25%	51.000000	0.000000	115.000000	0.000000	30.000000	0.000000	213000.000000	0.900000	134.000000	0.000000	0.000000	73.000000	0.000000
50%	60.000000	0.000000	249.000000	0.000000	38.000000	0.000000	262000.000000	1.100000	137.000000	1.000000	0.000000	115.000000	0.000000
75%	70.000000	1.000000	582.000000	1.000000	45.000000	1.000000	304000.000000	1.400000	140.000000	1.000000	1.000000	205.000000	1.000000
max	95.000000	1.000000	7861.000000	1.000000	65.000000	1.000000	850000.000000	9.400000	148.000000	1.000000	1.000000	285.000000	1.000000

x = df.iloc[:, [4,7,11]].values
y = df.iloc[:,-1].values

print(x)

[[ 20.     1.9    4.  ]
 [ 38.     1.1    6.  ]
 [ 20.     1.3    7.  ]
 [ 20.     1.9    7.  ]
 [ 20.     2.7    8.  ]
 [ 40.     2.1    8.  ]
 [ 15.     1.2   10.  ]
 [ 60.     1.1   10.  ]
 [ 65.     1.5   10.  ]
 [ 35.     9.4   10.  ]
 [ 38.     4.    10.  ]
 [ 25.     0.9   10.  ]
 [ 30.     1.1   11.  ]
 [ 38.     1.1   11.  ]
 [ 30.     1.    12.  ]
 [ 50.     1.3   13.  ]
 [ 38.     0.9   14.  ]
 [ 14.     0.8   14.  ]
 [ 25.     1.    15.  ]
 [ 55.     1.9   15.  ]
 [ 25.     1.3   16.  ]
 [ 30.     1.6   20.  ]
 [ 35.     0.9   20.  ]
 [ 60.     0.8   22.  ]
 [ 30.     1.83  23.  ]
 [ 38.     1.9   23.  ]
 [ 40.     1.    24.  ]
 [ 45.     1.3   26.  ]
 [ 38.     5.8   26.  ]
 [ 30.     1.2   26.  ]
 [ 38.     1.83  27.  ]
 [ 45.     3.    28.  ]
 [ 35.     1.    28.  ]
 [ 30.     1.2   29.  ]
 [ 50.     1.    29.  ]
 [ 35.     3.5   30.  ]
 [ 50.     1.    30.  ]
 [ 50.     1.    30.  ]
 [ 30.     2.3   30.  ]
 [ 38.     3.    30.  ]
 [ 20.     1.83  31.  ]
 [ 30.     1.2   32.  ]
 [ 45.     1.2   33.  ]
 [ 50.     1.    33.  ]
 [ 60.     1.1   33.  ]
 [ 38.     1.9   35.  ]
 [ 25.     0.9   38.  ]
 [ 38.     0.6   40.  ]
 [ 20.     4.4   41.  ]
 [ 30.     1.    42.  ]
 [ 25.     1.    43.  ]
 [ 20.     1.4   43.  ]
 [ 62.     6.8   43.  ]
 [ 50.     1.    44.  ]
 [ 38.     2.2   45.  ]
 [ 30.     2.    50.  ]
 [ 35.     2.7   54.  ]
 [ 40.     0.6   54.  ]
 [ 20.     1.1   55.  ]
 [ 20.     1.3   59.  ]
 [ 25.     1.    60.  ]
 [ 40.     2.3   60.  ]
 [ 35.     1.1   60.  ]
 [ 35.     1.    61.  ]
 [ 20.     2.9   64.  ]
 [ 15.     1.3   65.  ]
 [ 25.     1.    65.  ]
 [ 25.     1.2   66.  ]
 [ 25.     1.83  67.  ]
 [ 40.     0.8   68.  ]
 [ 35.     0.9   71.  ]
 [ 35.     1.    72.  ]
 [ 50.     1.3   72.  ]
 [ 20.     1.2   73.  ]
 [ 20.     0.7   73.  ]
 [ 60.     0.8   74.  ]
 [ 40.     1.2   74.  ]
 [ 38.     0.6   74.  ]
 [ 45.     0.9   74.  ]
 [ 40.     1.7   75.  ]
 [ 50.     1.18  76.  ]
 [ 25.     2.5   77.  ]
 [ 50.     1.8   78.  ]
 [ 25.     1.    78.  ]
 [ 50.     0.7   79.  ]
 [ 35.     1.1   79.  ]
 [ 60.     0.8   79.  ]
 [ 40.     0.7   79.  ]
 [ 25.     1.1   79.  ]
 [ 45.     0.8   80.  ]
 [ 45.     1.    80.  ]
 [ 60.     1.18  82.  ]
 [ 25.     1.7   82.  ]
 [ 38.     0.7   83.  ]
 [ 60.     1.    83.  ]
 [ 25.     1.3   83.  ]
 [ 60.     1.1   85.  ]
 [ 25.     1.2   85.  ]
 [ 40.     1.1   86.  ]
 [ 25.     1.1   87.  ]
 [ 45.     1.18  87.  ]
 [ 25.     1.1   87.  ]
 [ 30.     1.    87.  ]
 [ 50.     2.3   87.  ]
 [ 30.     1.7   88.  ]
 [ 45.     1.3   88.  ]
 [ 35.     0.9   88.  ]
 [ 38.     1.1   88.  ]
 [ 35.     1.3   88.  ]
 [ 60.     1.2   90.  ]
 [ 35.     1.2   90.  ]
 [ 25.     1.6   90.  ]
 [ 60.     1.3   90.  ]
 [ 40.     1.2   91.  ]
 [ 40.     1.    91.  ]
 [ 60.     0.7   94.  ]
 [ 60.     3.2   94.  ]
 [ 60.     0.9   94.  ]
 [ 38.     1.83  95.  ]
 [ 60.     1.5   95.  ]
 [ 38.     1.    95.  ]
 [ 38.     0.75  95.  ]
 [ 30.     0.9   95.  ]
 [ 40.     3.7   96.  ]
 [ 50.     1.3   97.  ]
 [ 17.     2.1  100.  ]
 [ 60.     0.8  104.  ]
 [ 30.     0.7  104.  ]
 [ 35.     3.4  105.  ]
 [ 60.     0.7  106.  ]
 [ 45.     6.1  107.  ]
 [ 40.     1.18 107.  ]
 [ 60.     1.3  107.  ]
 [ 35.     1.18 107.  ]
 [ 40.     1.18 107.  ]
 [ 60.     0.9  107.  ]
 [ 25.     2.1  108.  ]
 [ 35.     1.   108.  ]
 [ 30.     0.8  108.  ]
 [ 38.     1.1  109.  ]
 [ 35.     0.9  109.  ]
 [ 30.     0.9  109.  ]
 [ 40.     0.9  110.  ]
 [ 25.     1.7  111.  ]
 [ 30.     0.7  112.  ]
 [ 30.     0.7  112.  ]
 [ 60.     1.   113.  ]
 [ 30.     1.83 113.  ]
 [ 35.     0.9  115.  ]
 [ 45.     2.5  115.  ]
 [ 60.     0.9  117.  ]
 [ 45.     0.9  118.  ]
 [ 35.     1.18 119.  ]
 [ 35.     0.8  120.  ]
 [ 25.     1.7  120.  ]
 [ 35.     1.4  120.  ]
 [ 25.     1.   120.  ]
 [ 50.     1.3  121.  ]
 [ 45.     1.1  121.  ]
 [ 40.     1.2  121.  ]
 [ 35.     0.8  121.  ]
 [ 40.     0.9  123.  ]
 [ 35.     0.9  126.  ]
 [ 30.     1.1  129.  ]
 [ 38.     1.3  130.  ]
 [ 60.     0.7  134.  ]
 [ 20.     2.4  135.  ]
 [ 40.     1.   140.  ]
 [ 35.     0.8  145.  ]
 [ 35.     1.5  145.  ]
 [ 40.     0.9  146.  ]
 [ 60.     1.1  146.  ]
 [ 20.     0.8  146.  ]
 [ 35.     0.9  146.  ]
 [ 60.     1.   146.  ]
 [ 40.     1.   147.  ]
 [ 50.     1.   147.  ]
 [ 60.     1.2  147.  ]
 [ 40.     0.7  147.  ]
 [ 30.     0.9  148.  ]
 [ 25.     1.   150.  ]
 [ 25.     1.2  154.  ]
 [ 38.     2.5  162.  ]
 [ 25.     1.2  170.  ]
 [ 30.     1.5  171.  ]
 [ 50.     0.6  172.  ]
 [ 25.     2.1  172.  ]
 [ 40.     1.   172.  ]
 [ 45.     0.9  174.  ]
 [ 35.     2.1  174.  ]
 [ 60.     1.5  174.  ]
 [ 40.     0.7  175.  ]
 [ 30.     1.18 180.  ]
 [ 20.     1.6  180.  ]
 [ 45.     1.8  180.  ]
 [ 38.     1.18 185.  ]
 [ 30.     0.8  186.  ]
 [ 20.     1.   186.  ]
 [ 35.     1.8  186.  ]
 [ 45.     0.7  186.  ]
 [ 60.     1.   186.  ]
 [ 60.     0.9  186.  ]
 [ 25.     3.5  187.  ]
 [ 40.     0.7  187.  ]
 [ 45.     1.   187.  ]
 [ 40.     0.8  187.  ]
 [ 38.     0.9  187.  ]
 [ 40.     1.   187.  ]
 [ 35.     0.8  187.  ]
 [ 17.     1.   188.  ]
 [ 62.     0.8  192.  ]
 [ 50.     1.4  192.  ]
 [ 30.     1.6  193.  ]
 [ 35.     0.8  194.  ]
 [ 35.     1.3  195.  ]
 [ 50.     0.9  196.  ]
 [ 35.     1.1  197.  ]
 [ 35.     0.7  197.  ]
 [ 20.     1.83 198.  ]
 [ 50.     1.1  200.  ]
 [ 35.     1.1  201.  ]
 [ 25.     0.8  201.  ]
 [ 25.     1.   205.  ]
 [ 60.     1.4  205.  ]
 [ 25.     1.3  205.  ]
 [ 35.     1.   206.  ]
 [ 25.     5.   207.  ]
 [ 25.     1.2  207.  ]
 [ 30.     1.7  207.  ]
 [ 35.     1.1  208.  ]
 [ 35.     0.9  209.  ]
 [ 38.     1.4  209.  ]
 [ 45.     1.1  209.  ]
 [ 50.     1.1  209.  ]
 [ 50.     1.1  209.  ]
 [ 30.     1.2  210.  ]
 [ 40.     1.   210.  ]
 [ 45.     1.18 211.  ]
 [ 35.     1.3  212.  ]
 [ 30.     1.3  212.  ]
 [ 35.     1.1  212.  ]
 [ 40.     0.9  213.  ]
 [ 38.     1.8  213.  ]
 [ 38.     1.4  213.  ]
 [ 25.     1.1  214.  ]
 [ 25.     2.4  214.  ]
 [ 35.     1.   214.  ]
 [ 40.     1.2  214.  ]
 [ 30.     0.5  214.  ]
 [ 35.     0.8  215.  ]
 [ 45.     1.   215.  ]
 [ 35.     1.2  215.  ]
 [ 60.     1.   215.  ]
 [ 30.     1.   216.  ]
 [ 38.     1.7  220.  ]
 [ 38.     1.   230.  ]
 [ 25.     0.8  230.  ]
 [ 50.     0.7  231.  ]
 [ 40.     1.   233.  ]
 [ 40.     0.7  233.  ]
 [ 25.     1.4  235.  ]
 [ 60.     1.   237.  ]
 [ 38.     1.2  237.  ]
 [ 35.     0.9  240.  ]
 [ 20.     1.83 241.  ]
 [ 38.     1.7  244.  ]
 [ 38.     0.9  244.  ]
 [ 35.     1.   244.  ]
 [ 30.     1.6  244.  ]
 [ 40.     0.9  244.  ]
 [ 38.     1.2  245.  ]
 [ 40.     0.7  245.  ]
 [ 30.     1.   245.  ]
 [ 38.     0.8  245.  ]
 [ 35.     1.1  245.  ]
 [ 38.     1.1  246.  ]
 [ 30.     0.7  246.  ]
 [ 38.     1.3  246.  ]
 [ 40.     1.   247.  ]
 [ 40.     2.7  250.  ]
 [ 30.     3.8  250.  ]
 [ 38.     1.1  250.  ]
 [ 40.     0.8  250.  ]
 [ 40.     1.2  250.  ]
 [ 35.     1.7  250.  ]
 [ 55.     1.   250.  ]
 [ 35.     1.1  256.  ]
 [ 38.     0.9  256.  ]
 [ 55.     0.8  257.  ]
 [ 35.     1.4  258.  ]
 [ 38.     1.   258.  ]
 [ 35.     0.9  270.  ]
 [ 38.     1.1  270.  ]
 [ 38.     1.2  271.  ]
 [ 60.     0.8  278.  ]
 [ 38.     1.4  280.  ]
 [ 45.     1.6  285.  ]]

print(y)

[1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1
 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1
 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0
 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1
 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1
 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0
 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0]

# Splitting the dataset into training set and test set

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state =0)

#80% train, 20% test

x_train.shape, x_test.shape, y_train.shape, y_test.shape

((237, 3), (60, 3), (237,), (60,))

print(x_train)

[[ 40.     0.8  250.  ]
 [ 25.     2.5   77.  ]
 [ 35.     1.3   88.  ]
 [ 60.     1.   113.  ]
 [ 35.     0.7  197.  ]
 [ 25.     1.3   16.  ]
 [ 25.     0.9   38.  ]
 [ 35.     0.8  120.  ]
 [ 60.     1.4  205.  ]
 [ 35.     1.1  208.  ]
 [ 45.     1.3   26.  ]
 [ 20.     1.83 198.  ]
 [ 25.     0.8  230.  ]
 [ 50.     1.    30.  ]
 [ 25.     1.7   82.  ]
 [ 40.     1.2  121.  ]
 [ 45.     1.    80.  ]
 [ 40.     0.9  244.  ]
 [ 50.     1.3  121.  ]
 [ 35.     1.8  186.  ]
 [ 30.     1.3  212.  ]
 [ 50.     1.1  209.  ]
 [ 40.     1.2   74.  ]
 [ 25.     1.   205.  ]
 [ 40.     1.   140.  ]
 [ 35.     1.   206.  ]
 [ 25.     1.2   85.  ]
 [ 60.     1.1   33.  ]
 [ 35.     1.3  212.  ]
 [ 25.     1.2  154.  ]
 [ 35.     0.8  145.  ]
 [ 40.     1.   147.  ]
 [ 38.     2.2   45.  ]
 [ 60.     0.9  107.  ]
 [ 25.     1.    15.  ]
 [ 45.     0.9  174.  ]
 [ 25.     2.4  214.  ]
 [ 60.     3.2   94.  ]
 [ 38.     1.83  95.  ]
 [ 35.     1.    72.  ]
 [ 35.     1.   108.  ]
 [ 35.     1.1  212.  ]
 [ 60.     0.9  186.  ]
 [ 55.     1.   250.  ]
 [ 62.     0.8  192.  ]
 [ 40.     0.7  233.  ]
 [ 25.     1.   120.  ]
 [ 30.     1.2   29.  ]
 [ 25.     1.    78.  ]
 [ 60.     0.7  106.  ]
 [ 25.     1.    60.  ]
 [ 35.     1.18 119.  ]
 [ 55.     0.8  257.  ]
 [ 35.     1.2   90.  ]
 [ 30.     1.6   20.  ]
 [ 30.     1.2   26.  ]
 [ 40.     1.   187.  ]
 [ 38.     0.9   14.  ]
 [ 35.     2.7   54.  ]
 [ 60.     0.8   74.  ]
 [ 50.     1.3   97.  ]
 [ 30.     0.7  112.  ]
 [ 45.     1.1  121.  ]
 [ 60.     1.   215.  ]
 [ 60.     1.2   90.  ]
 [ 35.     0.9  146.  ]
 [ 20.     2.7    8.  ]
 [ 60.     1.1   85.  ]
 [ 30.     1.   245.  ]
 [ 40.     2.3   60.  ]
 [ 25.     1.2   66.  ]
 [ 40.     1.18 107.  ]
 [ 45.     1.8  180.  ]
 [ 20.     2.4  135.  ]
 [ 30.     0.7  246.  ]
 [ 20.     1.83  31.  ]
 [ 38.     2.5  162.  ]
 [ 38.     1.1   11.  ]
 [ 38.     1.1   88.  ]
 [ 40.     1.2  250.  ]
 [ 20.     1.9    7.  ]
 [ 25.     1.7  120.  ]
 [ 17.     2.1  100.  ]
 [ 30.     1.83  23.  ]
 [ 38.     1.83  27.  ]
 [ 38.     0.6   74.  ]
 [ 35.     1.1  245.  ]
 [ 50.     0.7  231.  ]
 [ 55.     1.9   15.  ]
 [ 35.     0.8  215.  ]
 [ 25.     5.   207.  ]
 [ 35.     0.9  240.  ]
 [ 50.     1.18  76.  ]
 [ 20.     1.4   43.  ]
 [ 20.     1.3    7.  ]
 [ 35.     0.9  209.  ]
 [ 30.     1.7   88.  ]
 [ 50.     1.   147.  ]
 [ 60.     0.8   79.  ]
 [ 38.     4.    10.  ]
 [ 35.     2.1  174.  ]
 [ 20.     1.1   55.  ]
 [ 30.     1.2   32.  ]
 [ 30.     1.    12.  ]
 [ 35.     1.4  120.  ]
 [ 25.     1.    43.  ]
 [ 38.     0.9  187.  ]
 [ 30.     1.7  207.  ]
 [ 40.     3.7   96.  ]
 [ 50.     0.9  196.  ]
 [ 35.     1.1   60.  ]
 [ 30.     1.5  171.  ]
 [ 25.     0.8  201.  ]
 [ 45.     6.1  107.  ]
 [ 35.     1.4  258.  ]
 [ 17.     1.   188.  ]
 [ 50.     1.    33.  ]
 [ 40.     1.    91.  ]
 [ 30.     0.8  108.  ]
 [ 35.     0.9  270.  ]
 [ 35.     0.8  194.  ]
 [ 45.     2.5  115.  ]
 [ 60.     1.3   90.  ]
 [ 38.     0.9  256.  ]
 [ 40.     1.1   86.  ]
 [ 30.     1.6  193.  ]
 [ 38.     0.7   83.  ]
 [ 60.     1.   186.  ]
 [ 35.     0.9  126.  ]
 [ 50.     1.    30.  ]
 [ 40.     0.7  147.  ]
 [ 40.     1.2   91.  ]
 [ 20.     1.9    4.  ]
 [ 60.     1.    83.  ]
 [ 25.     1.3   83.  ]
 [ 38.     1.2  271.  ]
 [ 40.     1.   233.  ]
 [ 30.     1.2  210.  ]
 [ 40.     0.8   68.  ]
 [ 30.     1.    42.  ]
 [ 20.     4.4   41.  ]
 [ 35.     1.1   79.  ]
 [ 60.     0.8  278.  ]
 [ 30.     0.9  109.  ]
 [ 60.     0.8   22.  ]
 [ 40.     0.9  213.  ]
 [ 35.     0.9  115.  ]
 [ 25.     1.7  111.  ]
 [ 45.     0.9   74.  ]
 [ 25.     1.   150.  ]
 [ 45.     1.18  87.  ]
 [ 45.     1.   187.  ]
 [ 40.     1.18 107.  ]
 [ 20.     1.83 241.  ]
 [ 38.     1.4  280.  ]
 [ 30.     0.8  186.  ]
 [ 15.     1.2   10.  ]
 [ 25.     1.83  67.  ]
 [ 40.     0.7  187.  ]
 [ 50.     0.7   79.  ]
 [ 40.     0.9  146.  ]
 [ 38.     0.75  95.  ]
 [ 35.     0.9  109.  ]
 [ 38.     1.1  270.  ]
 [ 40.     0.7  245.  ]
 [ 40.     0.9  110.  ]
 [ 30.     1.   216.  ]
 [ 60.     1.18  82.  ]
 [ 50.     1.8   78.  ]
 [ 30.     3.8  250.  ]
 [ 25.     0.9   10.  ]
 [ 60.     1.5   95.  ]
 [ 30.     1.    87.  ]
 [ 35.     3.5   30.  ]
 [ 40.     0.6   54.  ]
 [ 35.     1.5  145.  ]
 [ 38.     1.4  209.  ]
 [ 15.     1.3   65.  ]
 [ 38.     1.1    6.  ]
 [ 38.     1.    95.  ]
 [ 38.     1.2  237.  ]
 [ 25.     2.1  172.  ]
 [ 45.     1.2   33.  ]
 [ 45.     1.3   88.  ]
 [ 60.     1.3  107.  ]
 [ 40.     1.7   75.  ]
 [ 14.     0.8   14.  ]
 [ 25.     1.4  235.  ]
 [ 45.     1.1  209.  ]
 [ 30.     2.3   30.  ]
 [ 35.     1.18 107.  ]
 [ 50.     1.    44.  ]
 [ 38.     1.3  130.  ]
 [ 38.     1.7  220.  ]
 [ 35.     3.4  105.  ]
 [ 50.     1.    29.  ]
 [ 38.     5.8   26.  ]
 [ 25.     1.2  170.  ]
 [ 30.     1.1  129.  ]
 [ 45.     0.9  118.  ]
 [ 25.     1.1  214.  ]
 [ 25.     3.5  187.  ]
 [ 45.     3.    28.  ]
 [ 35.     1.    28.  ]
 [ 30.     0.7  104.  ]
 [ 50.     0.6  172.  ]
 [ 38.     1.1  246.  ]
 [ 30.     1.6  244.  ]
 [ 30.     1.83 113.  ]
 [ 38.     0.8  245.  ]
 [ 60.     1.2  147.  ]
 [ 25.     1.1   87.  ]
 [ 20.     1.   186.  ]
 [ 38.     1.4  213.  ]
 [ 60.     0.7   94.  ]
 [ 38.     1.7  244.  ]
 [ 50.     1.3   72.  ]
 [ 38.     1.9   23.  ]
 [ 60.     0.7  134.  ]
 [ 35.     1.7  250.  ]
 [ 60.     1.   146.  ]
 [ 35.     1.1  256.  ]
 [ 38.     3.    30.  ]
 [ 20.     1.6  180.  ]
 [ 25.     1.1   79.  ]
 [ 35.     0.9   71.  ]
 [ 40.     0.7   79.  ]
 [ 38.     1.8  213.  ]
 [ 38.     1.3  246.  ]
 [ 50.     1.4  192.  ]
 [ 35.     9.4   10.  ]
 [ 38.     1.18 185.  ]
 [ 35.     1.2  215.  ]
 [ 30.     1.18 180.  ]
 [ 60.     0.9   94.  ]
 [ 38.     0.6   40.  ]
 [ 20.     0.8  146.  ]]

print(y_test)

[0 0 1 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1
 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 0 0 1 0]

# Feature Scaling

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)

Model Building

Logistic Regression

# Logistic Regression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train, y_train)

LogisticRegression()

# Predicting the test set

y_pred = lr.predict(x_test)

# Making Confusion Matrix and calculating accuracy score

mylist = []
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test, y_pred)
mylist.append(ac)
print(cm)
print(ac)

[[40  3]
 [ 4 13]]
0.8833333333333333

lr_conf_matrix = confusion_matrix(y_test, y_pred)
lr_conf_matrix

array([[40,  3],
       [ 4, 13]], dtype=int64)

sns.heatmap(lr_conf_matrix, annot=True)
plt.xlabel('predicted')
plt.ylabel('Truth')

Text(70.0, 0.5, 'Truth')

tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()

print(f'True Neg: {tn}')
print(f'False Pos: {fp}')
print(f'False Neg: {fn}')
print(f'True Pos: {tp}')

True Neg: 40
False Pos: 3
False Neg: 4
True Pos: 13

lr_classification_report = classification_report(y_test, y_pred)
print(lr_classification_report)

              precision    recall  f1-score   support

           0       0.91      0.93      0.92        43
           1       0.81      0.76      0.79        17

    accuracy                           0.88        60
   macro avg       0.86      0.85      0.85        60
weighted avg       0.88      0.88      0.88        60

SVM

from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix, accuracy_score
list1 = []
for c in [0.5,0.6,0.7,0.8,0.9,1.0]:
    classifier = SVC(C = c, random_state=0, kernel = 'rbf')
    classifier.fit(x_train, y_train)
    y_pred = classifier.predict(x_test)
    list1.append(accuracy_score(y_test,y_pred))
plt.plot([0.5,0.6,0.7,0.8,0.9,1.0], list1)
plt.show()

# Training the Support Vector Classifier on the Training set

from sklearn.svm import SVC
classifier = SVC(C = 0.6, random_state=0, kernel = 'rbf')
classifier.fit(x_train, y_train)

SVC(C=0.6, random_state=0)

# Predicting the test set results

y_pred = classifier.predict(x_test)
print(y_pred)

[1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1
 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 1 0]

# Making the confusion matrix and calculating accuracy score

from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test, y_pred)
print(cm)
print(ac)
mylist.append(ac)

[[40  3]
 [ 3 14]]
0.9

svm_conf_matrix = confusion_matrix(y_test, y_pred)
svm_conf_matrix

array([[40,  3],
       [ 3, 14]], dtype=int64)

sns.heatmap(svm_conf_matrix, annot=True)
plt.xlabel('predicted')
plt.ylabel('Truth')

Text(70.0, 0.5, 'Truth')

tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()

print(f'True Neg: {tn}')
print(f'False Pos: {fp}')
print(f'False Neg: {fn}')
print(f'True Pos: {tp}')

True Neg: 40
False Pos: 3
False Neg: 3
True Pos: 14

svm_classification_report = classification_report(y_test, y_pred)
print(svm_classification_report)

              precision    recall  f1-score   support

           0       0.93      0.93      0.93        43
           1       0.82      0.82      0.82        17

    accuracy                           0.90        60
   macro avg       0.88      0.88      0.88        60
weighted avg       0.90      0.90      0.90        60

Random Forest

# Training the RandomForest Classifier on the Training set

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators = 11, criterion='entropy', random_state=0)
rf.fit(x_train,y_train)

RandomForestClassifier(criterion='entropy', n_estimators=11, random_state=0)

# Predicting the test set results

y_pred = rf.predict(x_test)
print(y_pred)

[1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1
 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 0 0 1 0]

# Making the confusion matrix and calculating the accuracy score

from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test, y_pred)
mylist.append(ac)
print(cm)
print(ac)

[[41  2]
 [ 1 16]]
0.95

rf_conf_matrix = confusion_matrix(y_test, y_pred)
rf_conf_matrix

array([[41,  2],
       [ 1, 16]], dtype=int64)

sns.heatmap(rf_conf_matrix, annot=True)
plt.xlabel('predicted')
plt.ylabel('Truth')

Text(70.0, 0.5, 'Truth')

tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()

print(f'True Neg: {tn}')
print(f'False Pos: {fp}')
print(f'False Neg: {fn}')
print(f'True Pos: {tp}')

True Neg: 41
False Pos: 2
False Neg: 1
True Pos: 16

rf_classification_report = classification_report(y_test, y_pred)
print(rf_classification_report)

              precision    recall  f1-score   support

           0       0.98      0.95      0.96        43
           1       0.89      0.94      0.91        17

    accuracy                           0.95        60
   macro avg       0.93      0.95      0.94        60
weighted avg       0.95      0.95      0.95        60

Naive Bayes

#Naive Bayes
from sklearn.naive_bayes import GaussianNB
nb = GaussianNB()
nb.fit(x_train, y_train)

GaussianNB()

y_pred = nb.predict(x_test)
print(y_pred)

[1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0]

# Making the confusion matrix and calculating the accuracy score

from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test, y_pred)
mylist.append(ac)
print(cm)
print(ac)

[[41  2]
 [ 6 11]]
0.8666666666666667

nb_conf_matrix = confusion_matrix(y_test, y_pred)
nb_conf_matrix

array([[41,  2],
       [ 6, 11]], dtype=int64)

sns.heatmap(nb_conf_matrix, annot=True)
plt.xlabel('predicted')
plt.ylabel('Truth')

Text(70.0, 0.5, 'Truth')

tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()

print(f'True Neg: {tn}')
print(f'False Pos: {fp}')
print(f'False Neg: {fn}')
print(f'True Pos: {tp}')

True Neg: 41
False Pos: 2
False Neg: 6
True Pos: 11

nb_classification_report = classification_report(y_test, y_pred)
print(nb_classification_report)

              precision    recall  f1-score   support

           0       0.87      0.95      0.91        43
           1       0.85      0.65      0.73        17

    accuracy                           0.87        60
   macro avg       0.86      0.80      0.82        60
weighted avg       0.86      0.87      0.86        60

Comparing Model Accuracy

# Plotting accuracy score of different models
mylist

[0.8833333333333333, 0.9, 0.95, 0.8666666666666667]

mylist2 = ["Logistic Regression", "SupportVector","RandomForest", 'Naive Bayes']

plt.rcParams['figure.figsize']=15,6 
sns.set_style("darkgrid")
ax = sns.barplot(x=mylist2, y=mylist, palette = "rocket", saturation =1.5)
plt.xlabel("ML Classifier Models", fontsize = 20 )
plt.ylabel("% of Accuracy", fontsize = 20)
plt.title("Accuracy of different Classifier Models", fontsize = 20)
plt.xticks(fontsize = 12, horizontalalignment = 'center', rotation = 8)
plt.yticks(fontsize = 13)
for p in ax.patches:
    width, height = p.get_width(), p.get_height()
    x, y = p.get_xy() 
    ax.annotate(f'{height:.2%}', (x + width/2, y + height*1.02), ha='center', fontsize = 'x-large')
plt.show()